In order to perform effective reflection removal, suitable priors should be exploited to constrain the problem effectively. Model-based methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]} formulate the image reflection removal problem as an optimization problem with explicitly defined image priors, for example, the gradient sparsity prior. Model-based methods lead to highly interpretable mathematical formulations and optimization algorithms though the end result may not be satisfactory when strong and complex reflections are present.
On the other hand, methods based on deep learning [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]} design task specific deep network structures and loss functions to exploit data-driven priors. These priors can be learned from large-scale real training data or from the generation of faithful synthetic training data.
However, the deep-learning based methods are difficult to interpret and a more principled approach to design the network structures is needed.
<FIGURE>